Overview

Dataset statistics

Number of variables39
Number of observations114827
Missing cells114827
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.7 MiB
Average record size in memory864.4 B

Variable types

BOOL18
CAT11
NUM10

Warnings

NextScore has constant value "114827" Constant
TeamWin has constant value "114827" Constant
IsSack has constant value "114827" Constant
IsMeasurement has constant value "114827" Constant
IsTwoPointConversion has constant value "114827" Constant
IsTwoPointConversionSuccessful has constant value "114827" Constant
IsNoPlay has constant value "114827" Constant
GameDate has a high cardinality: 197 distinct values High cardinality
Description has a high cardinality: 114808 distinct values High cardinality
SeasonYear is highly correlated with GameIdHigh correlation
GameId is highly correlated with SeasonYearHigh correlation
IsPass is highly correlated with IsRushHigh correlation
IsRush is highly correlated with IsPassHigh correlation
PenaltyYards is highly correlated with IsPenaltyAcceptedHigh correlation
IsPenaltyAccepted is highly correlated with PenaltyYardsHigh correlation
PassType has 46436 (40.4%) missing values Missing
RushDirection has 68391 (59.6%) missing values Missing
Description is uniformly distributed Uniform
Minute has 9359 (8.2%) zeros Zeros
Second has 5671 (4.9%) zeros Zeros
Yards has 27481 (23.9%) zeros Zeros
PenaltyYards has 113138 (98.5%) zeros Zeros

Reproduction

Analysis started2022-03-24 07:12:26.597747
Analysis finished2022-03-24 07:12:49.699114
Duration23.1 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct44755
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21625.389
Minimum0
Maximum46188
Zeros2
Zeros (%)< 0.1%
Memory size897.2 KiB
2022-03-24T01:12:49.771878image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2178
Q110711
median21185
Q332593
95-th percentile41691
Maximum46188
Range46188
Interquartile range (IQR)21882

Descriptive statistics

Standard deviation12723.4006
Coefficient of variation (CV)0.5883547622
Kurtosis-1.177328518
Mean21625.389
Median Absolute Deviation (MAD)10920
Skewness0.0662486437
Sum2483178543
Variance161884922.9
MonotocityNot monotonic
2022-03-24T01:12:49.877517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
249984< 0.1%
 
305944< 0.1%
 
105864< 0.1%
 
105884< 0.1%
 
189784< 0.1%
 
375334< 0.1%
 
305644< 0.1%
 
375274< 0.1%
 
305654< 0.1%
 
105974< 0.1%
 
305674< 0.1%
 
375024< 0.1%
 
106134< 0.1%
 
189644< 0.1%
 
106174< 0.1%
 
374984< 0.1%
 
374914< 0.1%
 
375414< 0.1%
 
375454< 0.1%
 
305564< 0.1%
 
189964< 0.1%
 
105514< 0.1%
 
105544< 0.1%
 
189984< 0.1%
 
105574< 0.1%
 
Other values (44730)11472799.9%
 
ValueCountFrequency (%) 
02< 0.1%
 
14< 0.1%
 
23< 0.1%
 
32< 0.1%
 
44< 0.1%
 
54< 0.1%
 
64< 0.1%
 
74< 0.1%
 
82< 0.1%
 
104< 0.1%
 
ValueCountFrequency (%) 
461881< 0.1%
 
461861< 0.1%
 
461841< 0.1%
 
461831< 0.1%
 
461821< 0.1%
 
461811< 0.1%
 
461761< 0.1%
 
461751< 0.1%
 
461741< 0.1%
 
461731< 0.1%
 

GameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct987
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019616215
Minimum2018090600
Maximum2021122700
Zeros0
Zeros (%)0.0%
Memory size897.2 KiB
2022-03-24T01:12:49.981672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2018090600
5-th percentile2018093000
Q12018123010
median2020091304
Q32021010309
95-th percentile2021120509
Maximum2021122700
Range3032100
Interquartile range (IQR)2887299

Descriptive statistics

Standard deviation1129091.088
Coefficient of variation (CV)0.0005590622019
Kurtosis-1.389889897
Mean2019616215
Median Absolute Deviation (MAD)999909
Skewness-0.01961024905
Sum2.319064711e+14
Variance1.274846686e+12
MonotocityNot monotonic
2022-03-24T01:12:50.086027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20191222081590.1%
 
20180930091530.1%
 
20190908091500.1%
 
20190929091490.1%
 
20181007031490.1%
 
20180930041480.1%
 
20200920011460.1%
 
20201025081460.1%
 
20181004001450.1%
 
20210926101450.1%
 
20181209051450.1%
 
20180909011440.1%
 
20180913001440.1%
 
20201004031420.1%
 
20211114041410.1%
 
20211017021410.1%
 
20201206041400.1%
 
20211003071400.1%
 
20181007051390.1%
 
20211212101390.1%
 
20211216001390.1%
 
20210909001360.1%
 
20201012001360.1%
 
20200920121360.1%
 
20200927061360.1%
 
Other values (962)11123996.9%
 
ValueCountFrequency (%) 
20180906001200.1%
 
20180909001190.1%
 
20180909011440.1%
 
20180909021200.1%
 
20180909031190.1%
 
20180909041200.1%
 
20180909051310.1%
 
20180909061090.1%
 
20180909071170.1%
 
20180909081250.1%
 
ValueCountFrequency (%) 
2021122700900.1%
 
20211226111170.1%
 
2021122610960.1%
 
20211226091290.1%
 
20211226081120.1%
 
20211226071250.1%
 
20211226061190.1%
 
20211226051260.1%
 
20211226041220.1%
 
20211226031140.1%
 

GameDate
Categorical

HIGH CARDINALITY

Distinct197
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
2021-01-03
 
1877
2018-12-30
 
1799
2020-09-20
 
1682
2021-10-03
 
1667
2019-12-15
 
1663
Other values (192)
106139 
ValueCountFrequency (%) 
2021-01-0318771.6%
 
2018-12-3017991.6%
 
2020-09-2016821.5%
 
2021-10-0316671.5%
 
2019-12-1516631.4%
 
2020-09-2716591.4%
 
2018-12-0916471.4%
 
2021-09-1216471.4%
 
2021-10-1016431.4%
 
2019-09-2216321.4%
 
2019-09-1516271.4%
 
2021-09-2616241.4%
 
2018-12-0216191.4%
 
2020-12-1316151.4%
 
2019-12-0816121.4%
 
2018-09-1616081.4%
 
2018-09-2315941.4%
 
2018-09-3015831.4%
 
2020-09-1315651.4%
 
2021-09-1915651.4%
 
2018-10-0715531.4%
 
2018-09-0915461.3%
 
2021-10-3115281.3%
 
2019-09-2915261.3%
 
2019-09-0815231.3%
 
Other values (172)7422364.6%
 
2022-03-24T01:12:50.206707image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:50.296281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
225377822.1%
 
125276422.0%
 
024431521.3%
 
-22965420.0%
 
9641215.6%
 
8409523.6%
 
3193681.7%
 
7117011.0%
 
5110221.0%
 
6107340.9%
 
498610.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number91861680.0%
 
Dash Punctuation22965420.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
225377827.6%
 
125276427.5%
 
024431526.6%
 
9641217.0%
 
8409524.5%
 
3193682.1%
 
7117011.3%
 
5110221.2%
 
6107341.2%
 
498611.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-229654100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1148270100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
225377822.1%
 
125276422.0%
 
024431521.3%
 
-22965420.0%
 
9641215.6%
 
8409523.6%
 
3193681.7%
 
7117011.0%
 
5110221.0%
 
6107340.9%
 
498610.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1148270100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
225377822.1%
 
125276422.0%
 
024431521.3%
 
-22965420.0%
 
9641215.6%
 
8409523.6%
 
3193681.7%
 
7117011.0%
 
5110221.0%
 
6107340.9%
 
498610.9%
 

Quarter
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.551612426
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size897.2 KiB
2022-03-24T01:12:50.363154image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.129643689
Coefficient of variation (CV)0.4427175844
Kurtosis-1.30296606
Mean2.551612426
Median Absolute Deviation (MAD)1
Skewness0.01464276003
Sum292994
Variance1.276094865
MonotocityNot monotonic
2022-03-24T01:12:50.430391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
23083426.9%
 
43052926.6%
 
32640123.0%
 
12632722.9%
 
57360.6%
 
ValueCountFrequency (%) 
12632722.9%
 
23083426.9%
 
32640123.0%
 
43052926.6%
 
57360.6%
 
ValueCountFrequency (%) 
57360.6%
 
43052926.6%
 
32640123.0%
 
23083426.9%
 
12632722.9%
 

Minute
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.736934693
Minimum0
Maximum15
Zeros9359
Zeros (%)8.2%
Memory size897.2 KiB
2022-03-24T01:12:50.508310image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.518951231
Coefficient of variation (CV)0.6707726046
Kurtosis-1.222770885
Mean6.736934693
Median Absolute Deviation (MAD)4
Skewness0.1251674468
Sum773582
Variance20.42092023
MonotocityNot monotonic
2022-03-24T01:12:50.579274image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
093598.2%
 
192788.1%
 
290417.9%
 
376786.7%
 
475126.5%
 
571286.2%
 
671026.2%
 
870866.2%
 
970606.1%
 
770146.1%
 
1169656.1%
 
1069316.0%
 
1268766.0%
 
1367515.9%
 
1463285.5%
 
1527182.4%
 
ValueCountFrequency (%) 
093598.2%
 
192788.1%
 
290417.9%
 
376786.7%
 
475126.5%
 
571286.2%
 
671026.2%
 
770146.1%
 
870866.2%
 
970606.1%
 
ValueCountFrequency (%) 
1527182.4%
 
1463285.5%
 
1367515.9%
 
1268766.0%
 
1169656.1%
 
1069316.0%
 
970606.1%
 
870866.2%
 
770146.1%
 
671026.2%
 

Second
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.52268195
Minimum0
Maximum59
Zeros5671
Zeros (%)4.9%
Memory size897.2 KiB
2022-03-24T01:12:50.672895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median28
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.73122096
Coefficient of variation (CV)0.6216533561
Kurtosis-1.203551012
Mean28.52268195
Median Absolute Deviation (MAD)15
Skewness0.02922598395
Sum3275174
Variance314.3961966
MonotocityNot monotonic
2022-03-24T01:12:50.773634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
056714.9%
 
5521051.8%
 
5420601.8%
 
5620461.8%
 
1920171.8%
 
1520071.7%
 
2419891.7%
 
2119831.7%
 
2219791.7%
 
5319671.7%
 
2019631.7%
 
2719511.7%
 
2519441.7%
 
1719321.7%
 
1819221.7%
 
1019211.7%
 
1319191.7%
 
1619111.7%
 
3019051.7%
 
818831.6%
 
2318791.6%
 
1418751.6%
 
4218741.6%
 
3218721.6%
 
2818691.6%
 
Other values (35)6238354.3%
 
ValueCountFrequency (%) 
056714.9%
 
116911.5%
 
217611.5%
 
317441.5%
 
417411.5%
 
517401.5%
 
617131.5%
 
718171.6%
 
818831.6%
 
918011.6%
 
ValueCountFrequency (%) 
5916821.5%
 
5816901.5%
 
5718001.6%
 
5620461.8%
 
5521051.8%
 
5420601.8%
 
5319671.7%
 
5217861.6%
 
5118391.6%
 
5017911.6%
 

OffenseTeam
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
DAL
 
3856
LA
 
3752
IND
 
3738
TB
 
3723
NE
 
3711
Other values (27)
96047 
ValueCountFrequency (%) 
DAL38563.4%
 
LA37523.3%
 
IND37383.3%
 
TB37233.2%
 
NE37113.2%
 
ATL37073.2%
 
PHI36973.2%
 
MIN36733.2%
 
PIT36563.2%
 
LAC36523.2%
 
KC36513.2%
 
CAR36343.2%
 
DET36263.2%
 
NO36233.2%
 
LV36143.1%
 
BAL36123.1%
 
SF35963.1%
 
CLE35853.1%
 
DEN35813.1%
 
CIN35733.1%
 
BUF35643.1%
 
CHI35483.1%
 
GB35463.1%
 
ARI35263.1%
 
JAX34963.0%
 
Other values (7)2388720.8%
 
2022-03-24T01:12:50.882242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:50.967560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.745565067
Min length2

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A3947712.5%
 
N3221510.2%
 
I288499.2%
 
L257788.2%
 
C216436.9%
 
E213756.8%
 
T181675.8%
 
D148014.7%
 
B144454.6%
 
H105743.4%
 
S104003.3%
 
P73532.3%
 
F71602.3%
 
R71602.3%
 
M71112.3%
 
G69822.2%
 
O69522.2%
 
J69212.2%
 
U68932.2%
 
Y68612.2%
 
K36511.2%
 
V36141.1%
 
X34961.1%
 
W33871.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter315265100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A3947712.5%
 
N3221510.2%
 
I288499.2%
 
L257788.2%
 
C216436.9%
 
E213756.8%
 
T181675.8%
 
D148014.7%
 
B144454.6%
 
H105743.4%
 
S104003.3%
 
P73532.3%
 
F71602.3%
 
R71602.3%
 
M71112.3%
 
G69822.2%
 
O69522.2%
 
J69212.2%
 
U68932.2%
 
Y68612.2%
 
K36511.2%
 
V36141.1%
 
X34961.1%
 
W33871.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin315265100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A3947712.5%
 
N3221510.2%
 
I288499.2%
 
L257788.2%
 
C216436.9%
 
E213756.8%
 
T181675.8%
 
D148014.7%
 
B144454.6%
 
H105743.4%
 
S104003.3%
 
P73532.3%
 
F71602.3%
 
R71602.3%
 
M71112.3%
 
G69822.2%
 
O69522.2%
 
J69212.2%
 
U68932.2%
 
Y68612.2%
 
K36511.2%
 
V36141.1%
 
X34961.1%
 
W33871.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII315265100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A3947712.5%
 
N3221510.2%
 
I288499.2%
 
L257788.2%
 
C216436.9%
 
E213756.8%
 
T181675.8%
 
D148014.7%
 
B144454.6%
 
H105743.4%
 
S104003.3%
 
P73532.3%
 
F71602.3%
 
R71602.3%
 
M71112.3%
 
G69822.2%
 
O69522.2%
 
J69212.2%
 
U68932.2%
 
Y68612.2%
 
K36511.2%
 
V36141.1%
 
X34961.1%
 
W33871.1%
 

DefenseTeam
Categorical

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
SEA
 
3755
NYG
 
3755
NYJ
 
3748
KC
 
3731
ARI
 
3679
Other values (27)
96159 
ValueCountFrequency (%) 
SEA37553.3%
 
NYG37553.3%
 
NYJ37483.3%
 
KC37313.2%
 
ARI36793.2%
 
MIN36733.2%
 
ATL36713.2%
 
DET36703.2%
 
MIA36693.2%
 
CIN36683.2%
 
CLE36563.2%
 
TEN36313.2%
 
HOU36253.2%
 
JAX36013.1%
 
LV35883.1%
 
DAL35873.1%
 
CHI35843.1%
 
PHI35833.1%
 
DEN35773.1%
 
TB35773.1%
 
PIT35733.1%
 
WAS35263.1%
 
LA35173.1%
 
IND35163.1%
 
GB35133.1%
 
Other values (7)2415421.0%
 
2022-03-24T01:12:51.062161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:51.147692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.753977723
Min length2

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A3940112.5%
 
N3250010.3%
 
I289459.2%
 
L249047.9%
 
E217166.9%
 
C216086.8%
 
T181225.7%
 
D143504.5%
 
B139514.4%
 
H107923.4%
 
S106733.4%
 
Y75032.4%
 
J73492.3%
 
M73422.3%
 
G72682.3%
 
R71902.3%
 
P71562.3%
 
O71302.3%
 
U70592.2%
 
F68262.2%
 
K37311.2%
 
X36011.1%
 
V35881.1%
 
W35261.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter316231100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A3940112.5%
 
N3250010.3%
 
I289459.2%
 
L249047.9%
 
E217166.9%
 
C216086.8%
 
T181225.7%
 
D143504.5%
 
B139514.4%
 
H107923.4%
 
S106733.4%
 
Y75032.4%
 
J73492.3%
 
M73422.3%
 
G72682.3%
 
R71902.3%
 
P71562.3%
 
O71302.3%
 
U70592.2%
 
F68262.2%
 
K37311.2%
 
X36011.1%
 
V35881.1%
 
W35261.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin316231100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A3940112.5%
 
N3250010.3%
 
I289459.2%
 
L249047.9%
 
E217166.9%
 
C216086.8%
 
T181225.7%
 
D143504.5%
 
B139514.4%
 
H107923.4%
 
S106733.4%
 
Y75032.4%
 
J73492.3%
 
M73422.3%
 
G72682.3%
 
R71902.3%
 
P71562.3%
 
O71302.3%
 
U70592.2%
 
F68262.2%
 
K37311.2%
 
X36011.1%
 
V35881.1%
 
W35261.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII316231100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A3940112.5%
 
N3250010.3%
 
I289459.2%
 
L249047.9%
 
E217166.9%
 
C216086.8%
 
T181225.7%
 
D143504.5%
 
B139514.4%
 
H107923.4%
 
S106733.4%
 
Y75032.4%
 
J73492.3%
 
M73422.3%
 
G72682.3%
 
R71902.3%
 
P71562.3%
 
O71302.3%
 
U70592.2%
 
F68262.2%
 
K37311.2%
 
X36011.1%
 
V35881.1%
 
W35261.1%
 

Down
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
1
52379 
2
38511 
3
21781 
4
 
2156
ValueCountFrequency (%) 
15237945.6%
 
23851133.5%
 
32178119.0%
 
421561.9%
 
2022-03-24T01:12:51.221523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:51.272974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:51.326999image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
15237945.6%
 
23851133.5%
 
32178119.0%
 
421561.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number114827100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
15237945.6%
 
23851133.5%
 
32178119.0%
 
421561.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common114827100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
15237945.6%
 
23851133.5%
 
32178119.0%
 
421561.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII114827100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
15237945.6%
 
23851133.5%
 
32178119.0%
 
421561.9%
 

ToGo
Real number (ℝ≥0)

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.523143512
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Memory size897.2 KiB
2022-03-24T01:12:51.410438image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median10
Q310
95-th percentile15
Maximum43
Range42
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.983234383
Coefficient of variation (CV)0.4673433432
Kurtosis2.837284447
Mean8.523143512
Median Absolute Deviation (MAD)1
Skewness0.5298511007
Sum978687
Variance15.86615615
MonotocityNot monotonic
2022-03-24T01:12:51.502388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
105725149.9%
 
166285.8%
 
556614.9%
 
754234.7%
 
653454.7%
 
449314.3%
 
849184.3%
 
344073.8%
 
243773.8%
 
942043.7%
 
1518201.6%
 
1118171.6%
 
1214531.3%
 
2013901.2%
 
1310640.9%
 
148880.8%
 
166340.6%
 
176140.5%
 
185670.5%
 
194230.4%
 
212010.2%
 
221660.1%
 
251620.1%
 
231240.1%
 
24980.1%
 
Other values (16)2610.2%
 
ValueCountFrequency (%) 
166285.8%
 
243773.8%
 
344073.8%
 
449314.3%
 
556614.9%
 
653454.7%
 
754234.7%
 
849184.3%
 
942043.7%
 
105725149.9%
 
ValueCountFrequency (%) 
431< 0.1%
 
412< 0.1%
 
404< 0.1%
 
391< 0.1%
 
382< 0.1%
 
362< 0.1%
 
355< 0.1%
 
346< 0.1%
 
339< 0.1%
 
329< 0.1%
 

YardLine
Real number (ℝ≥0)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4370749
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size897.2 KiB
2022-03-24T01:12:51.603455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q128
median46
Q369
95-th percentile94
Maximum100
Range99
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.80258776
Coefficient of variation (CV)0.5017001474
Kurtosis-0.9250604271
Mean49.4370749
Median Absolute Deviation (MAD)20
Skewness0.3306337404
Sum5676711
Variance615.1683598
MonotocityNot monotonic
2022-03-24T01:12:51.704958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2578256.8%
 
3019571.7%
 
3618691.6%
 
2018601.6%
 
4018371.6%
 
3418171.6%
 
3518021.6%
 
3117711.5%
 
3717131.5%
 
4517011.5%
 
3816741.5%
 
3216461.4%
 
2716231.4%
 
2916141.4%
 
2816001.4%
 
3315711.4%
 
4115571.4%
 
2615531.4%
 
3915451.3%
 
4414911.3%
 
5114721.3%
 
4614591.3%
 
4714491.3%
 
5514141.2%
 
4314111.2%
 
Other values (75)6759658.9%
 
ValueCountFrequency (%) 
11950.2%
 
22040.2%
 
31890.2%
 
42370.2%
 
53050.3%
 
62780.2%
 
72920.3%
 
84230.4%
 
94970.4%
 
106730.6%
 
ValueCountFrequency (%) 
1008070.7%
 
9914031.2%
 
987790.7%
 
977910.7%
 
968270.7%
 
958220.7%
 
946960.6%
 
937670.7%
 
927570.7%
 
918010.7%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
79434 
1
35393 
ValueCountFrequency (%) 
07943469.2%
 
13539330.8%
 
2022-03-24T01:12:51.769980image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

NextScore
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:51.793926image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Description
Categorical

HIGH CARDINALITY
UNIFORM

Distinct114808
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
(8:53) 8-B.OSWEILER PASS INCOMPLETE SHORT RIGHT.
 
2
(3:27) (SHOTGUN) 4-D.CARR PASS INCOMPLETE SHORT RIGHT.
 
2
(15:00) (SHOTGUN) 4-D.CARR PASS INCOMPLETE SHORT RIGHT.
 
2
(15:00) (SHOTGUN) 12-A.LUCK PASS INCOMPLETE SHORT LEFT TO 13-T.HILTON.
 
2
(2:00) (SHOTGUN) 15-P.MAHOMES PASS INCOMPLETE DEEP RIGHT TO 10-T.HILL.
 
2
Other values (114803)
114817 
ValueCountFrequency (%) 
(8:53) 8-B.OSWEILER PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(3:27) (SHOTGUN) 4-D.CARR PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(15:00) (SHOTGUN) 4-D.CARR PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(15:00) (SHOTGUN) 12-A.LUCK PASS INCOMPLETE SHORT LEFT TO 13-T.HILTON.2< 0.1%
 
(2:00) (SHOTGUN) 15-P.MAHOMES PASS INCOMPLETE DEEP RIGHT TO 10-T.HILL.2< 0.1%
 
(5:09) (SHOTGUN) 12-A.RODGERS PASS INCOMPLETE DEEP LEFT TO 17-D.ADAMS.2< 0.1%
 
(15:00) (SHOTGUN) 1-K.MURRAY PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(15:00) 12-A.RODGERS PASS INCOMPLETE DEEP RIGHT TO 83-M.VALDES-SCANTLING.2< 0.1%
 
(7:34) (SHOTGUN) 2-M.GLENNON PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(15:00) 8-K.COUSINS PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(3:14) (SHOTGUN) 8-K.COUSINS PASS INCOMPLETE DEEP RIGHT TO 19-A.THIELEN.2< 0.1%
 
(1:00) (SHOTGUN) 5-J.FLACCO PASS INCOMPLETE SHORT MIDDLE TO 15-M.CRABTREE.2< 0.1%
 
(11:54) 9-D.BREES PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(11:18) (SHOTGUN) 6-B.MAYFIELD PASS INCOMPLETE SHORT RIGHT TO 80-J.LANDRY.2< 0.1%
 
(15:00) (SHOTGUN) 7-B.ROETHLISBERGER PASS INCOMPLETE SHORT RIGHT TO 18-D.JOHNSON.2< 0.1%
 
(2:06) (SHOTGUN) 12-A.RODGERS PASS INCOMPLETE SHORT LEFT TO 33-A.JONES.2< 0.1%
 
(6:17) (SHOTGUN) 7-B.ROETHLISBERGER PASS INCOMPLETE SHORT MIDDLE TO 22-N.HARRIS.2< 0.1%
 
(14:23) 12-T.BRADY PASS INCOMPLETE DEEP RIGHT TO 13-M.EVANS.2< 0.1%
 
(8:23) (SHOTGUN) 12-A.RODGERS PASS INCOMPLETE SHORT RIGHT.2< 0.1%
 
(8:23) (NO HUDDLE) 4-D.WATSON PASS SHORT LEFT TO 87-D.FELLS TO IND 30 FOR 8 YARDS (97-A.MUHAMMAD).1< 0.1%
 
(4:25) (SHOTGUN) 7-J.BRISSETT PASS INCOMPLETE DEEP RIGHT TO 13-T.HILTON.1< 0.1%
 
(2:35) 25-M.MACK LEFT END TO IND 18 FOR -2 YARDS (55-B.MCKINNEY, 59-W.MERCILUS).1< 0.1%
 
(9:40) (SHOTGUN) 4-D.WATSON PASS SHORT LEFT INTENDED FOR 25-D.JOHNSON INTERCEPTED BY 35-P.DESIR [99-J.HOUSTON] AT IND 34. 35-P.DESIR TO IND 34 FOR NO GAIN (25-D.JOHNSON).1< 0.1%
 
(9:31) 25-M.MACK LEFT END RAN OB AT IND 35 FOR 1 YARD (59-W.MERCILUS).1< 0.1%
 
(2:44) (PUNT FORMATION) 9-B.ANGER UP THE MIDDLE RAN OB IN END ZONE FOR -5 YARDS, SAFETY (14-Z.PASCAL).1< 0.1%
 
Other values (114783)114783> 99.9%
 
2022-03-24T01:12:52.091995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique114789 ?
Unique (%)> 99.9%
2022-03-24T01:12:52.205323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length633
Median length88
Mean length94.37341392
Min length41

Overview of Unicode Properties

Unique unicode characters56
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
150837013.9%
 
O6241375.8%
 
T5952165.5%
 
S5543725.1%
 
R5277584.9%
 
E4834074.5%
 
A4698124.3%
 
.4510454.2%
 
N3789243.5%
 
D3757083.5%
 
-3345943.1%
 
H3136872.9%
 
L3054882.8%
 
)2857572.6%
 
(2857562.6%
 
I2847322.6%
 
12388382.2%
 
G2033151.9%
 
21946721.8%
 
U1899061.8%
 
P1862241.7%
 
F1854901.7%
 
M1637661.5%
 
31618971.5%
 
C1590561.5%
 
Other values (31)137468912.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter653172860.3%
 
Space Separator150837013.9%
 
Decimal Number126266811.7%
 
Other Punctuation6150715.7%
 
Dash Punctuation3345943.1%
 
Close Punctuation2920892.7%
 
Open Punctuation2920882.7%
 
Math Symbol8< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(28575697.8%
 
[62752.1%
 
{57< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
123883818.9%
 
219467215.4%
 
316189712.8%
 
414776111.7%
 
51239929.8%
 
0900007.1%
 
8875386.9%
 
9850636.7%
 
7697975.5%
 
6631105.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.45104573.3%
 
:11498018.7%
 
,335925.5%
 
;145372.4%
 
#5050.1%
 
'3250.1%
 
"70< 0.1%
 
&11< 0.1%
 
/6< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)28575797.8%
 
]62752.1%
 
}57< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1508370100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-334594100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O6241379.6%
 
T5952169.1%
 
S5543728.5%
 
R5277588.1%
 
E4834077.4%
 
A4698127.2%
 
N3789245.8%
 
D3757085.8%
 
H3136874.8%
 
L3054884.7%
 
I2847324.4%
 
G2033153.1%
 
U1899062.9%
 
P1862242.9%
 
F1854902.8%
 
M1637662.5%
 
C1590562.4%
 
Y1547462.4%
 
B1005231.5%
 
J846471.3%
 
K805071.2%
 
W674801.0%
 
V215780.3%
 
Z106360.2%
 
X74620.1%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=450.0%
 
+337.5%
 
>112.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin653172860.3%
 
Common430488839.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
150837035.0%
 
.45104510.5%
 
-3345947.8%
 
)2857576.6%
 
(2857566.6%
 
12388385.5%
 
21946724.5%
 
31618973.8%
 
41477613.4%
 
51239922.9%
 
:1149802.7%
 
0900002.1%
 
8875382.0%
 
9850632.0%
 
7697971.6%
 
6631101.5%
 
,335920.8%
 
;145370.3%
 
[62750.1%
 
]62750.1%
 
#505< 0.1%
 
'325< 0.1%
 
"70< 0.1%
 
{57< 0.1%
 
}57< 0.1%
 
Other values (5)25< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O6241379.6%
 
T5952169.1%
 
S5543728.5%
 
R5277588.1%
 
E4834077.4%
 
A4698127.2%
 
N3789245.8%
 
D3757085.8%
 
H3136874.8%
 
L3054884.7%
 
I2847324.4%
 
G2033153.1%
 
U1899062.9%
 
P1862242.9%
 
F1854902.8%
 
M1637662.5%
 
C1590562.4%
 
Y1547462.4%
 
B1005231.5%
 
J846471.3%
 
K805071.2%
 
W674801.0%
 
V215780.3%
 
Z106360.2%
 
X74620.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10836616100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
150837013.9%
 
O6241375.8%
 
T5952165.5%
 
S5543725.1%
 
R5277584.9%
 
E4834074.5%
 
A4698124.3%
 
.4510454.2%
 
N3789243.5%
 
D3757083.5%
 
-3345943.1%
 
H3136872.9%
 
L3054882.8%
 
)2857572.6%
 
(2857562.6%
 
I2847322.6%
 
12388382.2%
 
G2033151.9%
 
21946721.8%
 
U1899061.8%
 
P1862241.7%
 
F1854901.7%
 
M1637661.5%
 
31618971.5%
 
C1590561.5%
 
Other values (31)137468912.7%
 

TeamWin
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:52.272831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

SeasonYear
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
2020
30192 
2018
29284 
2021
27927 
2019
27424 
ValueCountFrequency (%) 
20203019226.3%
 
20182928425.5%
 
20212792724.3%
 
20192742423.9%
 
2022-03-24T01:12:52.320439image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:52.375402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:52.430310image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
217294637.7%
 
014501931.6%
 
18463518.4%
 
8292846.4%
 
9274246.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number459308100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
217294637.7%
 
014501931.6%
 
18463518.4%
 
8292846.4%
 
9274246.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common459308100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
217294637.7%
 
014501931.6%
 
18463518.4%
 
8292846.4%
 
9274246.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII459308100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
217294637.7%
 
014501931.6%
 
18463518.4%
 
8292846.4%
 
9274246.0%
 

Yards
Real number (ℝ)

ZEROS

Distinct115
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.323234083
Minimum-17
Maximum104
Zeros27481
Zeros (%)23.9%
Memory size897.2 KiB
2022-03-24T01:12:52.515615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-17
5-th percentile0
Q10
median4
Q39
95-th percentile23
Maximum104
Range121
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.904587775
Coefficient of variation (CV)1.408233138
Kurtosis13.4876997
Mean6.323234083
Median Absolute Deviation (MAD)4
Skewness2.891368689
Sum726078
Variance79.29168345
MonotocityNot monotonic
2022-03-24T01:12:52.620764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02748123.9%
 
377696.8%
 
276896.7%
 
472826.3%
 
568456.0%
 
166445.8%
 
658475.1%
 
750264.4%
 
843163.8%
 
943073.8%
 
1129952.6%
 
1027412.4%
 
1224092.1%
 
-122532.0%
 
1320591.8%
 
1418061.6%
 
1515341.3%
 
-214161.2%
 
1613301.2%
 
1711901.0%
 
1810660.9%
 
199360.8%
 
208690.8%
 
-38270.7%
 
216930.6%
 
Other values (90)74976.5%
 
ValueCountFrequency (%) 
-171< 0.1%
 
-131< 0.1%
 
-124< 0.1%
 
-118< 0.1%
 
-1014< 0.1%
 
-917< 0.1%
 
-837< 0.1%
 
-7790.1%
 
-61540.1%
 
-52960.3%
 
ValueCountFrequency (%) 
1041< 0.1%
 
1011< 0.1%
 
1001< 0.1%
 
992< 0.1%
 
982< 0.1%
 
972< 0.1%
 
961< 0.1%
 
941< 0.1%
 
931< 0.1%
 
923< 0.1%
 

Formation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
SHOTGUN
65201 
UNDER CENTER
39190 
NO HUDDLE SHOTGUN
8446 
NO HUDDLE
 
1985
WILDCAT
 
3
ValueCountFrequency (%) 
SHOTGUN6520156.8%
 
UNDER CENTER3919034.1%
 
NO HUDDLE SHOTGUN84467.4%
 
NO HUDDLE19851.7%
 
WILDCAT3< 0.1%
 
PUNT2< 0.1%
 
2022-03-24T01:12:52.720320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:52.779046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:52.848628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length7
Mean length9.476542973
Min length4

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N16246014.9%
 
E12800111.8%
 
U12327011.3%
 
T11284210.4%
 
H840787.7%
 
O840787.7%
 
R783807.2%
 
S736476.8%
 
G736476.8%
 
D600555.5%
 
580675.3%
 
C391933.6%
 
L104341.0%
 
W3< 0.1%
 
I3< 0.1%
 
A3< 0.1%
 
P2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter103009694.7%
 
Space Separator580675.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N16246015.8%
 
E12800112.4%
 
U12327012.0%
 
T11284211.0%
 
H840788.2%
 
O840788.2%
 
R783807.6%
 
S736477.1%
 
G736477.1%
 
D600555.8%
 
C391933.8%
 
L104341.0%
 
W3< 0.1%
 
I3< 0.1%
 
A3< 0.1%
 
P2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
58067100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin103009694.7%
 
Common580675.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N16246015.8%
 
E12800112.4%
 
U12327012.0%
 
T11284211.0%
 
H840788.2%
 
O840788.2%
 
R783807.6%
 
S736477.1%
 
G736477.1%
 
D600555.8%
 
C391933.8%
 
L104341.0%
 
W3< 0.1%
 
I3< 0.1%
 
A3< 0.1%
 
P2< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
58067100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1088163100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N16246014.9%
 
E12800111.8%
 
U12327011.3%
 
T11284210.4%
 
H840787.7%
 
O840787.7%
 
R783807.2%
 
S736476.8%
 
G736476.8%
 
D600555.5%
 
580675.3%
 
C391933.6%
 
L104341.0%
 
W3< 0.1%
 
I3< 0.1%
 
A3< 0.1%
 
P2< 0.1%
 

PlayType
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
PASS
68391 
RUSH
46436 
ValueCountFrequency (%) 
PASS6839159.6%
 
RUSH4643640.4%
 
2022-03-24T01:12:52.924032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:52.969409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:53.016567image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
S18321839.9%
 
P6839114.9%
 
A6839114.9%
 
R4643610.1%
 
U4643610.1%
 
H4643610.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter459308100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S18321839.9%
 
P6839114.9%
 
A6839114.9%
 
R4643610.1%
 
U4643610.1%
 
H4643610.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin459308100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S18321839.9%
 
P6839114.9%
 
A6839114.9%
 
R4643610.1%
 
U4643610.1%
 
H4643610.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII459308100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
S18321839.9%
 
P6839114.9%
 
A6839114.9%
 
R4643610.1%
 
U4643610.1%
 
H4643610.1%
 

IsRush
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
68391 
1
46436 
ValueCountFrequency (%) 
06839159.6%
 
14643640.4%
 
2022-03-24T01:12:53.066421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsPass
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
1
68391 
0
46436 
ValueCountFrequency (%) 
16839159.6%
 
04643640.4%
 
2022-03-24T01:12:53.091579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
92426 
1
22401 
ValueCountFrequency (%) 
09242680.5%
 
12240119.5%
 
2022-03-24T01:12:53.116523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
109604 
1
 
5223
ValueCountFrequency (%) 
010960495.5%
 
152234.5%
 
2022-03-24T01:12:53.141911image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

PassType
Categorical

MISSING

Distinct16
Distinct (%)< 0.1%
Missing46436
Missing (%)40.4%
Memory size897.2 KiB
SHORT RIGHT
22340 
SHORT LEFT
20480 
SHORT MIDDLE
13286 
DEEP RIGHT
5022 
DEEP LEFT
4679 
Other values (11)
2584 
ValueCountFrequency (%) 
SHORT RIGHT2234019.5%
 
SHORT LEFT2048017.8%
 
SHORT MIDDLE1328611.6%
 
DEEP RIGHT50224.4%
 
DEEP LEFT46794.1%
 
DEEP MIDDLE25622.2%
 
INTENDED FOR5< 0.1%
 
NOT LISTED5< 0.1%
 
MIDDLE TO3< 0.1%
 
BACK TO2< 0.1%
 
RIGHT TO2< 0.1%
 
[58-H.LANDRY III]1< 0.1%
 
MIDDLE [55-FCLARK]1< 0.1%
 
INTERCEPTED BY1< 0.1%
 
LEFT TO1< 0.1%
 
(SHOTGUN) 10-THILL1< 0.1%
 
(Missing)4643640.4%
 
2022-03-24T01:12:53.194639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2022-03-24T01:12:53.274054image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length10
Mean length7.576911354
Min length3

Overview of Unicode Properties

Unique unicode characters33
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
T10865712.5%
 
n9287210.7%
 
R834789.6%
 
H834739.6%
 
683917.9%
 
E655567.5%
 
O561256.5%
 
S561126.4%
 
a464365.3%
 
D439845.1%
 
I432315.0%
 
L410214.7%
 
G273653.1%
 
F251662.9%
 
M158521.8%
 
P122641.4%
 
N18< 0.1%
 
A4< 0.1%
 
C4< 0.1%
 
B3< 0.1%
 
K3< 0.1%
 
53< 0.1%
 
-3< 0.1%
 
[2< 0.1%
 
Y2< 0.1%
 
Other values (8)9< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter66231976.1%
 
Lowercase Letter13930816.0%
 
Space Separator683917.9%
 
Decimal Number6< 0.1%
 
Open Punctuation3< 0.1%
 
Dash Punctuation3< 0.1%
 
Close Punctuation3< 0.1%
 
Other Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T10865716.4%
 
R8347812.6%
 
H8347312.6%
 
E655569.9%
 
O561258.5%
 
S561128.5%
 
D439846.6%
 
I432316.5%
 
L410216.2%
 
G273654.1%
 
F251663.8%
 
M158522.4%
 
P122641.9%
 
N18< 0.1%
 
A4< 0.1%
 
C4< 0.1%
 
B3< 0.1%
 
K3< 0.1%
 
Y2< 0.1%
 
U1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
68391100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n9287266.7%
 
a4643633.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[266.7%
 
(133.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5350.0%
 
8116.7%
 
1116.7%
 
0116.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
]266.7%
 
)133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin80162792.1%
 
Common684077.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
T10865713.6%
 
n9287211.6%
 
R8347810.4%
 
H8347310.4%
 
E655568.2%
 
O561257.0%
 
S561127.0%
 
a464365.8%
 
D439845.5%
 
I432315.4%
 
L410215.1%
 
G273653.4%
 
F251663.1%
 
M158522.0%
 
P122641.5%
 
N18< 0.1%
 
A4< 0.1%
 
C4< 0.1%
 
B3< 0.1%
 
K3< 0.1%
 
Y2< 0.1%
 
U1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
68391> 99.9%
 
53< 0.1%
 
-3< 0.1%
 
[2< 0.1%
 
]2< 0.1%
 
81< 0.1%
 
.1< 0.1%
 
(1< 0.1%
 
)1< 0.1%
 
11< 0.1%
 
01< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII870034100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
T10865712.5%
 
n9287210.7%
 
R834789.6%
 
H834739.6%
 
683917.9%
 
E655567.5%
 
O561256.5%
 
S561126.4%
 
a464365.3%
 
D439845.1%
 
I432315.0%
 
L410214.7%
 
G273653.1%
 
F251662.9%
 
M158521.8%
 
P122641.4%
 
N18< 0.1%
 
A4< 0.1%
 
C4< 0.1%
 
B3< 0.1%
 
K3< 0.1%
 
53< 0.1%
 
-3< 0.1%
 
[2< 0.1%
 
Y2< 0.1%
 
Other values (8)9< 0.1%
 

IsSack
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:53.329560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114324 
1
 
503
ValueCountFrequency (%) 
011432499.6%
 
15030.4%
 
2022-03-24T01:12:53.350735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114629 
1
 
198
ValueCountFrequency (%) 
011462999.8%
 
11980.2%
 
2022-03-24T01:12:53.375226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsMeasurement
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:53.398467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
113213 
1
 
1614
ValueCountFrequency (%) 
011321398.6%
 
116141.4%
 
2022-03-24T01:12:53.419197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsFumble
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
113785 
1
 
1042
ValueCountFrequency (%) 
011378599.1%
 
110420.9%
 
2022-03-24T01:12:53.443600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsPenalty
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
111896 
1
 
2931
ValueCountFrequency (%) 
011189697.4%
 
129312.6%
 
2022-03-24T01:12:53.468693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsTwoPointConversion
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:53.492003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsTwoPointConversionSuccessful
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:53.511533image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

RushDirection
Categorical

MISSING

Distinct7
Distinct (%)< 0.1%
Missing68391
Missing (%)59.6%
Memory size897.2 KiB
CENTER
12923 
RIGHT GUARD
6127 
LEFT GUARD
5748 
LEFT END
5653 
RIGHT TACKLE
5500 
Other values (2)
10485 
ValueCountFrequency (%) 
CENTER1292311.3%
 
RIGHT GUARD61275.3%
 
LEFT GUARD57485.0%
 
LEFT END56534.9%
 
RIGHT TACKLE55004.8%
 
LEFT TACKLE54924.8%
 
RIGHT END49934.3%
 
(Missing)6839159.6%
 
2022-03-24T01:12:53.561484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:53.622025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:53.693907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length3
Mean length5.435664086
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n13678221.9%
 
a6839111.0%
 
E6437710.3%
 
T574289.2%
 
R414186.6%
 
335135.4%
 
G284954.6%
 
L278854.5%
 
C239153.8%
 
N235693.8%
 
A228673.7%
 
D225213.6%
 
F168932.7%
 
I166202.7%
 
H166202.7%
 
U118751.9%
 
K109921.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter38547561.8%
 
Lowercase Letter20517332.9%
 
Space Separator335135.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n13678266.7%
 
a6839133.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E6437716.7%
 
T5742814.9%
 
R4141810.7%
 
G284957.4%
 
L278857.2%
 
C239156.2%
 
N235696.1%
 
A228675.9%
 
D225215.8%
 
F168934.4%
 
I166204.3%
 
H166204.3%
 
U118753.1%
 
K109922.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
33513100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin59064894.6%
 
Common335135.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n13678223.2%
 
a6839111.6%
 
E6437710.9%
 
T574289.7%
 
R414187.0%
 
G284954.8%
 
L278854.7%
 
C239154.0%
 
N235694.0%
 
A228673.9%
 
D225213.8%
 
F168932.9%
 
I166202.8%
 
H166202.8%
 
U118752.0%
 
K109921.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
33513100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII624161100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n13678221.9%
 
a6839111.0%
 
E6437710.3%
 
T574289.2%
 
R414186.6%
 
335135.4%
 
G284954.6%
 
L278854.5%
 
C239153.8%
 
N235693.8%
 
A228673.7%
 
D225213.6%
 
F168932.7%
 
I166202.7%
 
H166202.7%
 
U118751.9%
 
K109921.8%
 

YardLineFixed
Real number (ℝ≥0)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.81095039
Minimum0
Maximum50
Zeros807
Zeros (%)0.7%
Memory size897.2 KiB
2022-03-24T01:12:53.782322image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median29
Q339
95-th percentile48
Maximum50
Range50
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.90370155
Coefficient of variation (CV)0.4478749009
Kurtosis-0.7373369364
Mean28.81095039
Median Absolute Deviation (MAD)10
Skewness-0.3414147642
Sum3308275
Variance166.5055137
MonotocityNot monotonic
2022-03-24T01:12:53.885272image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2589507.8%
 
4032002.8%
 
4531152.7%
 
3030872.7%
 
3530382.6%
 
3430152.6%
 
3629472.6%
 
3728942.5%
 
2028432.5%
 
4928292.5%
 
3828052.4%
 
3927872.4%
 
4427692.4%
 
4127592.4%
 
3127242.4%
 
4827152.4%
 
3226852.3%
 
4726732.3%
 
3326612.3%
 
4626612.3%
 
2826542.3%
 
4326352.3%
 
2926342.3%
 
2725952.3%
 
4225772.2%
 
Other values (26)3857533.6%
 
ValueCountFrequency (%) 
08070.7%
 
115981.4%
 
29830.9%
 
39800.9%
 
410640.9%
 
511271.0%
 
69740.8%
 
710590.9%
 
811801.0%
 
912981.1%
 
ValueCountFrequency (%) 
507350.6%
 
4928292.5%
 
4827152.4%
 
4726732.3%
 
4626612.3%
 
4531152.7%
 
4427692.4%
 
4326352.3%
 
4225772.2%
 
4127592.4%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
OWN
63032 
OPP
51795 
ValueCountFrequency (%) 
OWN6303254.9%
 
OPP5179545.1%
 
2022-03-24T01:12:53.973843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-03-24T01:12:54.023250image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:54.070034image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O11482733.3%
 
P10359030.1%
 
W6303218.3%
 
N6303218.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter344481100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O11482733.3%
 
P10359030.1%
 
W6303218.3%
 
N6303218.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin344481100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O11482733.3%
 
P10359030.1%
 
W6303218.3%
 
N6303218.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII344481100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O11482733.3%
 
P10359030.1%
 
W6303218.3%
 
N6303218.3%
 

IsPenaltyAccepted
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
113085 
1
 
1742
ValueCountFrequency (%) 
011308598.5%
 
117421.5%
 
2022-03-24T01:12:54.119909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

IsNoPlay
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size897.2 KiB
0
114827 
ValueCountFrequency (%) 
0114827100.0%
 
2022-03-24T01:12:54.143760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

PenaltyYards
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1703693382
Minimum0
Maximum22
Zeros113138
Zeros (%)98.5%
Memory size897.2 KiB
2022-03-24T01:12:54.188589image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.469663935
Coefficient of variation (CV)8.626340574
Kurtosis82.21575462
Mean0.1703693382
Median Absolute Deviation (MAD)0
Skewness9.012220869
Sum19563
Variance2.159912081
MonotocityNot monotonic
2022-03-24T01:12:54.262159image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
011313898.5%
 
157560.7%
 
104840.4%
 
51650.1%
 
1441< 0.1%
 
831< 0.1%
 
1329< 0.1%
 
1226< 0.1%
 
1125< 0.1%
 
924< 0.1%
 
121< 0.1%
 
420< 0.1%
 
718< 0.1%
 
616< 0.1%
 
212< 0.1%
 
311< 0.1%
 
165< 0.1%
 
182< 0.1%
 
211< 0.1%
 
221< 0.1%
 
171< 0.1%
 
ValueCountFrequency (%) 
011313898.5%
 
121< 0.1%
 
212< 0.1%
 
311< 0.1%
 
420< 0.1%
 
51650.1%
 
616< 0.1%
 
718< 0.1%
 
831< 0.1%
 
924< 0.1%
 
ValueCountFrequency (%) 
221< 0.1%
 
211< 0.1%
 
182< 0.1%
 
171< 0.1%
 
165< 0.1%
 
157560.7%
 
1441< 0.1%
 
1329< 0.1%
 
1226< 0.1%
 
1125< 0.1%
 

Interactions

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Correlations

2022-03-24T01:12:54.358810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-24T01:12:54.572507image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-24T01:12:54.785989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-24T01:12:55.000904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-24T01:12:47.901571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:48.777582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:49.288220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-03-24T01:12:49.456376image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Sample

First rows

df_indexGameIdGameDateQuarterMinuteSecondOffenseTeamDefenseTeamDownToGoYardLineSeriesFirstDownNextScoreDescriptionTeamWinSeasonYearYardsFormationPlayTypeIsRushIsPassIsIncompleteIsTouchdownPassTypeIsSackIsChallengeIsChallengeReversedIsMeasurementIsInterceptionIsFumbleIsPenaltyIsTwoPointConversionIsTwoPointConversionSuccessfulRushDirectionYardLineFixedYardLineDirectionIsPenaltyAcceptedIsNoPlayPenaltyYards
0020210926122021-09-261910MINSEA1105710(9:10) 8-K.COUSINS PASS SHORT RIGHT TO 83-T.CONKLIN TO SEA 26 FOR 17 YARDS (21-T.FLOWERS).0202117UNDER CENTERPASS0100SHORT RIGHT000000000NaN43OPP000
1120210926122021-09-261832MINSEA1107400(8:32) 8-K.COUSINS PASS SHORT LEFT TO 18-J.JEFFERSON TO SEA 18 FOR 8 YARDS (21-T.FLOWERS, 33-J.ADAMS).020218UNDER CENTERPASS0100SHORT LEFT000000000NaN26OPP000
2220210926122021-09-261752MINSEA228210(7:52) (SHOTGUN) 8-K.COUSINS PASS SHORT LEFT TO 18-J.JEFFERSON TO SEA 8 FOR 10 YARDS (21-T.FLOWERS).0202110SHOTGUNPASS0100SHORT LEFT000000000NaN18OPP000
3320210926122021-09-261713MINSEA189200(7:13) (SHOTGUN) 25-A.MATTISON UP THE MIDDLE TO SEA 7 FOR 1 YARD (33-J.ADAMS).020211SHOTGUNRUSH1000NaN000000000CENTER8OPP000
4420211010082021-10-101950WASNO225110(9:50) (SHOTGUN) 24-A.GIBSON LEFT TACKLE TO NO 46 FOR 3 YARDS (97-M.ROACH).020213SHOTGUNRUSH1000NaN000000000LEFT TACKLE49OPP000
5520211010082021-10-101911WASNO1105400(9:11) (SHOTGUN) 4-T.HEINICKE PASS SHORT LEFT TO 24-A.GIBSON PUSHED OB AT NO 37 FOR 9 YARDS (20-P.WERNER).020219SHOTGUNPASS0100SHORT LEFT000000000NaN46OPP000
6620211017062021-10-173119LANYG275400(1:19) (SHOTGUN) 9-M.STAFFORD PASS SHORT RIGHT TO 1-D.JACKSON TO NYG 40 FOR 6 YARDS (24-J.BRADBERRY).020216SHOTGUNPASS0100SHORT RIGHT000000000NaN46OPP000
7720211024052021-10-244934KCTEN485810(9:34) (SHOTGUN) 15-P.MAHOMES PASS DEEP MIDDLE TO 13-B.PRINGLE TO TEN 17 FOR 25 YARDS (21-M.FARLEY; 24-E.MOLDEN).0202125SHOTGUNPASS0100DEEP MIDDLE000000000NaN42OPP000
8820211031012021-10-314453BUFMIA1107700(4:53) 20-Z.MOSS RIGHT TACKLE TO MIA 20 FOR 3 YARDS (70-A.BUTLER, 15-J.PHILLIPS).020213UNDER CENTERRUSH1000NaN000000000RIGHT TACKLE23OPP000
91020211024082021-10-2421243ARIHOU245810(12:43) (NO HUDDLE, SHOTGUN) 2-C.EDMONDS UP THE MIDDLE TO HOU 31 FOR 11 YARDS (20-JU.REID, 37-T.THOMAS).0202111NO HUDDLE SHOTGUNRUSH1000NaN000000000CENTER42OPP000

Last rows

df_indexGameIdGameDateQuarterMinuteSecondOffenseTeamDefenseTeamDownToGoYardLineSeriesFirstDownNextScoreDescriptionTeamWinSeasonYearYardsFormationPlayTypeIsRushIsPassIsIncompleteIsTouchdownPassTypeIsSackIsChallengeIsChallengeReversedIsMeasurementIsInterceptionIsFumbleIsPenaltyIsTwoPointConversionIsTwoPointConversionSuccessfulRushDirectionYardLineFixedYardLineDirectionIsPenaltyAcceptedIsNoPlayPenaltyYards
1148174494920180916122018-09-16360JAXNE243610(6:00) 5-B.BORTLES PASS SHORT MIDDLE TO 17-D.CHARK TO JAX 49 FOR 13 YARDS (24-S.GILMORE). FUMBLES (24-S.GILMORE), RECOVERED BY NE-21-D.HARMON AT NE 48. 21-D.HARMON TO JAX 48 FOR 4 YARDS (72-J.WELLS).0201813UNDER CENTERPASS0100SHORT MIDDLE000001000NaN36OWN000
1148184495120180916132018-09-164545NYGDAL1102510(5:45) (SHOTGUN) 10-E.MANNING PASS SHORT LEFT TO 88-E.ENGRAM TO NYG 36 FOR 11 YARDS (55-L.VANDER ESCH).0201811SHOTGUNPASS0100SHORT LEFT000000000NaN25OWN000
1148194495320180916102018-09-1641038DETSF1103000(10:38) 9-M.STAFFORD PASS INCOMPLETE DEEP MIDDLE TO 11-M.JONES.020180UNDER CENTERPASS0110DEEP MIDDLE000000000NaN30OWN000
1148204495620180916092018-09-1621437LAARI218910(14:37) (NO HUDDLE) 30-T.GURLEY RIGHT END FOR 11 YARDS, TOUCHDOWN.0201811NO HUDDLERUSH1001NaN000000000RIGHT END11OPP000
1148214496620180916042018-09-161116MIANYJ319410(1:16) 32-K.DRAKE UP THE MIDDLE FOR 6 YARDS, TOUCHDOWN.020186UNDER CENTERRUSH1001NaN000000000CENTER6OPP000
1148224497420180916012018-09-163118BUFLAC119910(11:08) 33-C.IVORY UP THE MIDDLE FOR 1 YARD, TOUCHDOWN.020181UNDER CENTERRUSH1001NaN000000000CENTER1OPP000
1148234497820180910002018-09-10304DETNYJ1103810(:04) (NO HUDDLE, SHOTGUN) 9-M.STAFFORD PASS SHORT LEFT TO 11-M.JONES TO NYJ 49 FOR 13 YARDS (36-D.MIDDLETON).0201813NO HUDDLE SHOTGUNPASS0100SHORT LEFT000000000NaN38OWN000
1148244497920180910002018-09-10370DETNYJ1102500(7:00) (SHOTGUN) 9-M.STAFFORD PASS INCOMPLETE SHORT LEFT TO 15-G.TATE.020180SHOTGUNPASS0110SHORT LEFT000000000NaN25OWN000
1148254499120180909092018-09-0931426ARIWAS1105200(14:26) 31-D.JOHNSON UP THE MIDDLE TO WAS 47 FOR 1 YARD (95-D.PAYNE).020181UNDER CENTERRUSH1000NaN000000000CENTER48OPP000
1148264499320180909082018-09-09278KCLAC272810(7:08) (SHOTGUN) 15-P.MAHOMES RIGHT END TO KC 36 FOR 8 YARDS (98-I.ROCHELL).020188SHOTGUNRUSH1000NaN000000000RIGHT END28OWN000